This proposal aims to develop 3D anatomical analysis tools of the developing mouse brain, combining the 3D alignment precision of MRI imaging and the higher resolution of serial histological section images. The special new features of the MRI imaging will be the implementation of diffusion tensor imaging (DTI), a new type of MRI that can provide unique image contrast based on measurement of water diffusion. Since water tends to diffuse along oriented structures within a volume, DTI serves effectively to delineate various early structures within the brain. This is a particularly important technical advance for the immature brain which has not yet formed myelin, because the brain at these stages of development has little intrinsic contrast detectable by conventional MRI. In this proposal, we will focus on development of technologies and databases that will be essential components for the long-term goal through the following four aims. First, MRI-based database of developing embryo and neonates will be acquired from days 11 to 19 of gestation, at 3-day intervals postnatally to day 21, and weekly thereafter to 12 weeks of age. Second, the slowness of acquisition of the very large datasets accessible by DTI will be offset by combination of a modified fast spin echo technique that will reduce acquisition time by a factor of 8, and parallel imaging that will reduce the time factor by 2. Third, a large series of serially-sectioned and stained mouse brains at all stages from embryonic day 11 to adulthood are available, and section images will be digitally captured and elastically warped to the MRI standards, based on common anatomical landmarks. From this study, MR-visible structures will be identified and assigned. Then the aligned MRI and histological images will be segmented into cell groups and axonal tracts by a combination of automated "seeding" algorithms and "neuroanatomical expert" hand-tracing of contours by modifications of Unix-based 3D software that we have developed for the adult mouse brain. Finally, tools for computational neuroanatomy will be developed for reliable quantitative measures of morphological differences between specimens. This project was made possible through collaboration with Drs. Moil (MRI), Sidman (neuroanatomy), and Miller (computational neuroanatomy).